For miR-34a, miR-298, and miR -892b, we observed cell phenotypes (such as cell retraction and detachment) suggestive of cell death. Therefore, we directly tested the ability of several candidate miRs to induce apoptotic signaling. Indeed, miR-34a, miR-298, and miR-892b overexpression activated caspase 3/7 by ~4- to 7-fold, as compared with the mock-transfected control (Figure 5B). The data suggested that these miRs suppress growth in part by inducing apoptotic cell death. The miRs that did not induce caspase activation may instead repress cell proliferation or promote caspase-independent cell death.

This proof-of-principle use of our miR-HTS identified a set of 59 miR candidates that inhibited growth of the IMR90 human fetal lung fibroblast cell line in our functional screen. Approximately 35% of the growth-inhibitory miR candidates identified herein have been implicated previously in growth suppression in IMR90 and/or other lung cancer cell lines, including reports that directly evaluated cellular mechanism experimentally (i.e., cell cycle, apoptosis, senescence assays), and reports that observed expression changes associated with growth-inhibitory function (i.e., upregulated expression in senescent cells versus young cells, or downregulated expression in lungcancer samples versus normal counterpart cells; summarized and referenced in Supplementary Table S4). Fifteen of these 59 candidate miRs are known to be upregulated in senescent IMR90 cells, including miR-449a/b (45). Ten of these 59 candidate miRs, including four upregulated in senescent IMR90 cells, are known to be downregulated in lung cancers, including miR-7 (46). Finally, four of the candidate miRs have been shown to promote apoptosis, promote senescence, or inhibit cell cycling in lung cancer cell lines (Supplementary Table S4). It will be interesting to examine whether any of the 11 novel growth-inhibitory miR candidates identified from IMR90 (i.e., the 11 miRs noted with “D” in the column labeled “Known Expression Pattern” of Supplementary Table S4) have tumor-suppressive activity in lung cancers that have downregulated expression of these specific miRs.

Intriguingly, the miR-HTS co-identified three pairs of candidates that are paralogs encoding the same miR hairpin sequence. Having exactly the same mature miR sequence, these paralogous miRs recognize the same targets and thus would be predicted to have the same effect on cell growth. These paralogous candidates include the miR-550a-1 and miR-550a-2 paralogs (miR-550a-3 was not included in the library used), miR-513a-1 and miR-513a-2 paralogs, and the miR-128-1 and miR-128-2 paralogs (Supplementary Table S4). In addition, multiple members of three additional miR families (i.e., beyond the above three sets of paralogs) were identified in the set of growth-inhibitory candidates [e.g., miR-888, miR-892a, and miR-892b of the miR-743 family (Supplementary Table S4)]. As a result of the miR hairpin sequence homology within each of these miR families (47), each miR family member may recognize many of the same targets and have similar functions. Moreover, the 59 growth-inhibitory candidates include miR-34a, miR-34c, and the miR-449a~449b cluster, which belong to two different miR families, but share identical seed sequence. We were surprised to find that miR-34b, which is also a member of the miR-34 family, was not among the candidates identified by the miR-HTS. Overexpression of miR-34b caused ≥8-fold decrease in abundance at the last time point in only one of the two miR-HTS replicate screens.

In summary, we have established miR-HTS as a novel methodology to conduct unbiased high-throughput miR functional screens. The 75% validation rate among the 12 selected candidates of a proof of-principle screen in the IMR90 cell line demonstrated the high fidelity of this approach. It should be possible to apply this miR-HTS technology to assess the roles of miRs in cellular processes other than growth-inhibition, targeting any depletion- or enrichment-based phenotype (e.g., one could identify the set of miRs that enhance differentiation using stage/lineage-specific surface markers to enrich the differentiated fraction from the entire cell population). The estimated costs per sample for miR-HTS is less than half that of other miR barcode quantification methods (see comparison in Supplementary Table S5). In addition, as the lenti-miR library expands, we can readily add more GRE-qPCR assays. Thus, this novel lenti-miR library–based and qPCR-based miR-HTS strategy provides a flexible and reliable platform for miR functional screening, with lower complexity and costs compared with published methods.

Acknowledgments

We thank Gerald Vandergrift at Applied Biosystems for expert advice on GRE-qPCR assay designs and development, Fernando Pineda at Johns Hopkins University for expert advice on statistics, and all Civin lab members for helpful discussions. This work was supported by NIH/NCI grant #P01CA70970 to C.C. This paper is subject to the NIH Public Access Policy.